Machine learning has come a long way from becoming a buzzword to a way of communication of the business processes. The terms in technology trends that we hear every day related to artificial intelligence or machine learning are now buzzwords in the technology market. These terms or buzzwords are often misinterpreted or misunderstood and used in place of each other due to lack of knowledge.
As the market is growing for machine learning, the internet is stirring up with trends and buzzwords. We have jolted down top 10 machine learning buzzwords and their meanings before they become part of every business in the coming years –
- Artificial Intelligence
- Machine Learning
- Deep Learning
- Big Data
- Data Science
- Data Analytics
- Internet of Things
- Data Mining
- Data Modeling
- Predictive Analysis
When a system or more specifically a computer program that mimics or simulates the human decision-making process and performs tasks somewhat the way a human being could do, then that is called artificial intelligence.
AI systems are virtually divided into two categories – General and Narrow AI. General AI systems have major capabilities of human intelligence, such as understanding of languages, recognizing objects, shapes, and sounds, learning things, planning and solving problems to some extent. Narrow AI systems have some of the human intelligence features, and that is developed and defined as required.
Example – Customer service process with inbuilt AI. When a customer calls to the service desk, the AI system interprets the query of the customer based on the inputs provided and extracts the information from the records. The customer service agent can work faster through the relevant documents in hand and it increases customer experience. It also saves a lot of time and money.
In machine learning, systems use the already existing data, analyses them to teach a program to detect the patterns, in order to learn, define and predict new processes. ML is a subset of AI and it is used as a tool to build intelligent AI applications, Through ML algorithms, computing devices or software gain capability of learning things their own without being programmed to do so.
Example – Data of the sales calls can be clustered on the basis of the time of the day the call was placed and keyword classification in the quarries through machine learning algorithms. This helps marketers to find new strategies for ad campaigns for increasing sales.
Deep learning is one of the many ways machine learning can be implemented and it is an advanced approach to machine learning. Deep learning algorithm uses a similar structure and functionality of the human brain. It consists of nodes or neurons like a human brain and connected to each other for communication of information. This is called an artificial neural network system. It has separate layers for learning each skill and these layers create depth in the system which is different from other machine learning systems. This is why it is named deep learning.
Big data certainly refers to a large or huge amount of data. After the internet of things has arrived or we can say after the rebirth of internet usage techniques, data has become voluptuous, huge and more meaningful. However, big data does not always have to be about the quality of data. It can be the volume of the data, velocity or variety of data. This amount of data cannot be handled by existing RDBMS systems. It needs different storage systems and smart algorithms to handle them.
Data science is a product or future of artificial intelligence we can say. DS uses smart and intelligent algorithms, data, analytics and statistical methods to extract unstructured data from different sources and produce structured data for further processing and investigation. It needs the blend of machine learning, data mining, programming and statistical computation processes to function.
Data analytics is a process of analyzing a chunk of data to provide qualitative and quantitative outputs for the enhancement of productivity and gain of the businesses. It is a technique where raw and unstructured data is extracted and categorized according to the specific business needs and provides data and patterns to the businesses. The enterprises use that data sets for examination and drawing a conclusion about the contained information, to achieve specific business gains.
Internet of things is a technology that connects objects to the system to acquire data. Objects can be devices, machines, computing devices, animals or even human beings. An identifier or sensor is provided to them, data of each and every activity gets transferred over the network. However, the data that is resulted from the IoT systems is huge and it needs big data systems and machine learning algorithms to make good use of it.
Data mining is a way to analyze and evaluate very large information or data to create a new set of data. This resultant data is created as per the requirements and relevance to accomplish some other function. Enterprises use data mining techniques and tools to predict the business trends that help them make better business decisions.
A model is a bunch of information or data that are structured in a specific way according to a particular demographic or interests to the user. Modeling is a machine learning approach to create a model. For example: when someone places ads through the Google Adwords, they can use already existing models for specific demographics or specific keywords, search results for faster processing.
Predictive analysis is a blend of IoT, machine learning, and data analytics. Predictive analysis uses the data from IoT computing devices, interpret and analyze them through data analytics techniques and makes future trend predictions using machine learning algorithms. Marketers use a huge amount of unstructured data for using future trend predictions for businesses and the market.
Everyone loves to text and hear back from machines. That’s why chatbots and chatbot development are hyped. Chatbots (may or may not) have multiple machine learning implementations, deployed in their creation. Having special benefits for the businesses, chatbots can engage a lot more audience than your human executives, provided – it is meant for the same.
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